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A Bayesian Whittaker–Henderson smoother for general‐purpose and sample‐based spectral baseline estimation and peak extraction
Author(s) -
Lau Sok Kiang,
Winlove Peter,
Moger Julian,
Champion Olivia L.,
Titball Richard W.,
Yang Zi Hua,
Yang Zheng Rong
Publication year - 2012
Publication title -
journal of raman spectroscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.748
H-Index - 110
eISSN - 1097-4555
pISSN - 0377-0486
DOI - 10.1002/jrs.3165
Subject(s) - baseline (sea) , bayesian probability , background subtraction , raman spectroscopy , subtraction , sample (material) , extraction (chemistry) , prior probability , computer science , mathematics , spectral line , algorithm , artificial intelligence , pattern recognition (psychology) , statistics , chemistry , optics , physics , geology , arithmetic , chromatography , oceanography , astronomy , pixel
Raman spectroscopy is a well‐established technique that allows both chemical and structural analysis of materials. Raman spectra are often complex and extracting meaningful information is easily hindered by spectral interferences; one of the most significant sources being variations in background. Raman spectra have diverse sources of background making it hard to eliminate them or theoretically to predict the form of the baseline, which frequently varies between samples. Although many different methods for baseline removal have been proposed, most require some form of user input. User input is also subjective and consequently less reproducible than automated methods and variations in baseline subtraction can distort peak heights leading to erroneous results. We present a Bayesian Whittaker–Henderson smoother for spectral baseline estimation and peak extraction. It is a generalisation of the Whittaker–Henderson smoother, a regularised regression algorithm. We introduce hierarchical priors for model parameters of the smoother and propose a global aligner for consistent peak extraction across multiple spectra. We show that this novel smoother significantly outperforms several existing smoothers. Copyright © 2012 John Wiley & Sons, Ltd.